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GeneMapper® Software Version 4.1 Reference and

1. Chapter 2 SNPlex System Troubleshooting Algorithms 33 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide wu Chapter 2 SNPlex System Troubleshooting Overview Overview Identifying Potential Problems Resolving Problems and Errors 34 You can use tools in the GeneMapper Software to identify potential problems at both the project and study levels of the SNPlex System analysis Identifying Potential Problems in SNPlex System Studies Version 4 1 of the GeneMapper Software emphasizes the use of studies for analyzing data generated using the SNPlex Genotyping System Chemistries You can use the tools of the Study Manager to maintain system wide quality control and visualize potential problems in SNPlex System data After you identify a potential problem the software allows you to review the applicable run and resolve the issues that can be corrected Note See the GeneMapper Software Version 4 1 SNPlex System Analysis Getting Started Guide PN 4403617 for a detailed explanation of the study management system Identifying Potential Problems in SNPlex Projects SNPlex projects like the other analyses supported by the GeneMapper Software contain a variety of Process Quality Values PQVs which can aid you in identifying potential problems Chapter 1 explains the functions of all PQVs relevant to the analysis of SNPlex System data The SNPlex Geno
2. OBA One Basepair Allele Description Function Expected Values Troubleshooting 18 The OBA PQV indicates for the associated genotype that the apex of the associated peak is present at a position within 1 base pair of another peak J Pass or A Check Select the affected genotype click W Analysis Display Plots then review the allele at the appropriate bin location for a microvariant peak or an invalid allele call GeneMapper Software Version 4 1 Reference and Troubleshooting Guide OS Offscale Chapter 1 Process Quality Values and Basic Troubleshooting OS Offscale ni Description The OS PQV is displayed in both the Samples and Genotypes tabs of Function the GeneMapper window but the function of the OS PQV in each tab differs in the following way OS PQV for the Samples tab The signal associated with the size standard ofthe specified sample contains one or more peaks that exceed the maximum detectable range OS PQV for the Genotypes Tab The signals associated with the given sample contain one or more peaks that exceed the maximum detectable range Note When the OS PQV of the Genotypes tab is triggered the software reduces the GQ PQV by 50 the default multiplier is 0 5 Expected Values jj Pass or A Check Troubleshooting 1 2 In the GeneMapper window select the Samples tab In the Navigation Pane click to expand the project folder then select
3. Note Table 1 2 lists only the information contained in the Info tab that is relevant to troubleshooting For a complete description of the elements listed in the Info tab see the GeneMapper Software Online Help GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 5 iden Chapter 1 Process Quality Values and Basic Troubleshooting Diagnosing and Resolving Basic Problems Table 1 2 Info Tab information relevant to troubleshooting Group Description Information Sample Describes the source and status of the imported sample data Information Sample Origin Path Displays the path to the associated sample file at the time it was imported provided that the sample was added to the project from a sample file Status Message Displays information related to any events that occurred when the sample was imported into the project Error Message Displays any errors the software encounters during the analysis of the associated sample You can use the information in this group to verify the source of several analysis problems Current Settings Describes the analysis settings currently applied to the associated sample All of the data displayed in this group is useful for troubleshooting problems with the GeneMapper Software Although most of the information can be viewed in various parts of the software the Current Settings group summarizes all the information in a single location for easier access
4. Bars indicate detected peaks T 200 S x d i 8 soo I l T E AA 2 NI o E 0 lt DN J a kad DOES Modan V CL ad is Ara AA REN UT ail aaa ania 341 l i4 lI L bae l 0 400 800 1200 1600 scan line Figure 3 14 Size matching example 46 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 3 Algorithms Wu Size Calling Methods Classic and Advanced Modes Size Calling Methods Classic and Advanced Modes Types of Size The GeneMapper Software provides the following size calling methods Calling Methods E Least Squares Method 2nd and 3rd Order 47 B Cubic Spline Interpolation Method luus 49 B Local Southern Method eanan 50 B Global Southern Method sssllsele lesse 52 Least Squares Method Overview Both Least Squares methods 2nd Order and 3rd Order use regression analysis to build a best fit size calling curve This curve compensates for any fragments that may run anomalously Consequently this method typically results in the least amount of deviation for all the fragments including the size standards and the samples Depending on whether you choose the 2nd or 3rd Order Least Squares Method in the Analysis Parameters dialog box the resulting size curve is either a quadratic or a cubic function The software uses the known standard fragments and the associated data points to pro
5. GeneMapper Software has not undergone specific developmental validation for human identification applications Human identification laboratories analyzing single source or parentage samples which choose to use GeneMapper Software for data analysis should perform their own developmental validation studies The AFLP process is covered by patents owned by Keygene N V TRADEMARKS Applied Biosystems AB Design ABI PRISM GeneMapper GeneScan Primer Focus and SNaPshot are registered trademarks and GeneScan and SNPlex are trademarks of Applied Biosystems or its affiliates in the U S and or certain other countries AFLP is a registered trademark of Keygene N V This product includes software developed by the Apache Software Foundation http www apache org Copyright O 1999 2000 The Apache Software Foundation All rights reserved This product includes software developed by the ExoLab Project http www exolab org Copyright 2000 O Intalio Inc All rights reserved JNIRegistry is Copyright 1997 Timothy Gerard Endres ICE Engineering Inc http www trustice com Oracle is a registered trademark of Oracle Corporation All other trademarks are the sole property of their respective owners Copyright 2009 Applied Biosystems All rights reserved Part Number 4403673 Rev A 04 2009 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Contents Preface V How to Use This Guide een V How t
6. audit event definition 61 Index audit map definition 61 audit object definition 61 audit record definition 61 autopanelizer definition 61 B BD Broad Peak PQV 9 BIN Out of Bin Allele PQV 10 bold text when to use v Broad Peak PQV 9 C cache definition 61 CC Control Concordance POV 11 challenge definition 61 Control Concordance PQV 11 control security group definition 62 conventions bold text v for describing menu commands v IMPORTANTS vi in this guide v italic text v Notes vi user attention words vi Cross Talk PQV 32 customer feedback on Applied Biosystems documents viii D data access control definition 62 data flow genotyping algorithms 36 data group definition 62 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 67 Index data rights definition 62 database definition 62 documentation related vii Double Peak PQV 12 DP 12 DP Double Peak PQV 12 E EPT data displaying 5 examining peak definitions 39 example output of different allele calling algorithms 54 G Genotype Quality PQV 13 genotyping algorithms 36 GQ Genotype Quality POV 13 H help online accessing vii Information Development department contacting viii italic text when to use v L LMS definition 63 Low Peak Height PQV 15 LPH Low Peak Height PQV 15 M Marker Quality 14 Matrix Not Found PQV 16 menu commands conventions for describing v MNF Matrix Not Found
7. 1 Reference and Troubleshooting Guide Chapter 1 Process Quality Values and Basic Troubleshooting S Diagnosing and Resolving Basic Problems Displaying By default the GeneMapper Software displays fl Pass A Check Numeric PQV or Low Quality in some PQV columns to represent the numeric Metrics score of the associated quality metric When troubleshooting quality errors it is often more useful to configure the software to display numeric representations of the quality values For example the Sizing Quality SQ PQV evaluates the similarity between the fragment pattern defined by the size standard definition and the actual size standard peak distribution pattern in the sample data The Sizing Quality metric yields a value between 1 and 0 that represents a combination of statistical measures for the size calling method used to perform the analysis Based on the PQV Threshold settings of the Quality Flags tab the software displays Pass A Check or Low Quality to indicate the result of the Sizing Quality calculation Sizing Quality Representation Samples Table Example Symbols RunName sFWF SNF os Tela default DGB_SNPlex_ H E DGB_SNPlex_ gm m E 1 Numbers RunName SFNF swF os SQ wELLo recommended for troubleshooting pce sw OG fio to DGB_SNPlex_ E E 10 1 0 To display numerical representations of the quality metrics 1 Select Tools gt Options th
8. 25 mm Chapter 1 Process Quality Values and Basic Troubleshooting SQ Sizing Quality Troubleshooting Review the data of the size standards that failed the SQ PQV 1 In the Samples tab of the GeneMapper window click Analysis gt Low Quality to Top to sort the data so that the samples that produced errors appear at the top of the table In the Samples tab select the rows for the sample s that display A Check or Fail in the SQ column Click Analysis gt Size Match Editor to view the sizing information for the selected sample s In the Navigation Pane of the Size Match Editor select a sample file to display the sizing data for the associated sample Review the data for the following qualities Signal Strength The signal strength peak height of all peaks must exceed the Peak Detection Threshold defined in the analysis method used to analyze the data Correct Size Calls Labels All peaks must be correctly identified by the software The labels above the peaks must be in sequential order from left to right least to greatest Evenness of Signal Strength All peaks should have relatively uniform signal strengths Sizing Quality The sizing quality of each sample should be within the passing range for your chemistry application Note To magnify the plot of the Size Matches tab drag the mouse cursor Q across a region of the x or y axis Use Table 1 4 o
9. POV 16 MSDSs obtaining viii N Narrow Bin PQV 18 NB Narrow Bin PQV 18 numeric quality metrics displaying 7 O OBA One Basepair Allele PQY 18 Offscale PQV 19 One Basepair Allele PQV 18 online help accessing vii optimizing peak detection sensitivity 41 OS Offscale PQV 19 Out of Bin Allele PQV 10 Overlap PQV 20 OVL Overlap PQV 20 P peak definitions examining 39 peak detection 38 effects of extreme settings 43 guidelines for use 39 optimizing sensitivity 41 parameters 38 peak window size 38 polynomial degree 38 slope threshold 44 peak detection sensitivity reducing window size 41 Peak Height RatioPQV 21 PHR Peak Height Ratio PQV 21 polynomial degree 38 peak detection 38 varying 39 window size value 40 possible local sizing inaccuracy 49 Process Quality Values PQV 2 to 32 displaying as numbers 7 profile definition 65 68 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide project settings definition 65 R Raw Data displaying 5 reducing window size and increasing polynomial degree 42 rights definition 65 S Sample File Not Found PQV 22 sample information displaying 5 security group definition 65 security ID definition 65 SFNF Sample File Not Found PQV 22 Sharp Peak PQV 23 SHP Sharp Peak PQV 23 silent auditing definition 65 Single Peak Artifact PQV 24 size calling 53 advanced method 47 classic method 47 cubic spline interpolation method 49 global
10. Provides information that is necessary for proper instrument operation accurate chemistry kit use or safe use of a chemical Examples of the user attention words appear below Note The size of the column affects the run time Note The Calibrate function is also available in the Control Console IMPORTANT To verify your client connection to the database you need a valid Oracle user ID and password IMPORTANT You must create a separate Sample Entry Spreadsheet for each 96 well plate How to Obtain More Information Safety See the GeneMapper Software Version 4 1 Installation and Information Administration Guide PN 4403614 for safety information Software See the GeneMapper Software Version 4 1 Installation and Warranty and Administration Guide PN 4403614 for warranty and licensing License information vi GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Preface How to Obtain More Information Related The following related documents are shipped with the software Documentation GeneMapper Software Version 4 1 Installation and Administration Guide PN 4403614 Provides procedures for installing securing and maintaining version 4 1 of the GeneMapper Software GeneMapper Software Version 4 1 Getting Started Guides for microsatellite analysis PN 4403672 loss of hetereozygosity LOH analysis PN 4403621 AFLP system analysis PN 4403620 SNaPshot kit analysi
11. T T T T 1000 1500 2000 2500 3000 3500 4000 4500 Figure 3 16 3rd Order Least Squares size calling curve 48 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 3 Algorithms Wu Size Calling Methods Classic and Advanced Modes Cubic Spline Interpolation Method Overview Possible Local Sizing Inaccuracy The Cubic Spline method forces the sizing curve through all the known points of the selected size standard Although this enforcement produces exact results for the values of the standards themselves it does not compensate for standard fragments that may run anomalously Best Fit 2nd Order Curve AO 9 771623E 01 Al 1 220838E 01 A2 4 856885E 06 R2 1 000 Size Calling Curve Cubic Spline Interpolation T T T T T T T T 1000 1500 2000 2500 3000 3500 4000 4500 Figure 3 17 Cubic Spline Interpolation Method Mobility of any DNA fragment can be affected by its sequence and by secondary and tertiary structure formation If any internal size standard fragment has anomalous mobility the Cubic Spline method may exhibit local sizing inaccuracy For example assume that a standard fragment is close in molecular length to an unknown sample fragment Assume further that the standard fragment runs anomalously The Cubic Spline method assigns the official value to this standard fragment even though it may be slightly incorrect The size of the unknown fragment is then likely to be calculated inco
12. in sample data in a project in the GeneMapper Software They are denoted by a blue asterisk in the Panel Manager Identification of the specific allelic form of a marker Identification of alleles based on bin definitions genotyping GeneMapper Software analysis A sample of DNA containing most possible alleles for a specific marker or set of markers Used to create a sample file that the GeneMapper Software can use to genotype or make allele calls on sample data Within the GeneMapper Software you select a Sample Type of Allelic Ladder for the sample file generated using an allelic ladder A collection of user defined parameters that determine the bin set and analysis algorithms A collection of user defined settings including analysis method size standard and panel that determine the sizing and genotyping algorithms used by the GeneMapper Software to analyze all sample files in a project Also called project settings GeneMapper Software Version 4 1 Reference and Troubleshooting Guide association audit event audit map audit object audit record autopanelizer bin bin set cache challenge chromosome Glossary Two identifiers combined are said to be associated A user can be associated with a user group A user group associated with a security group yields a set of data rights A single permanent change to one or more attributes of an object Includes creating a new instance of an object or delet
13. not the GeneMapper Software can access the size standard definition specified in the Size Standard column for the associated sample jg Pass or A Check Verify that the software does not contain the desired size standard 1 In the Samples tab of the GeneMapper window note the name of the size standard assigned to the affected sample 2 Click Tools GeneMapper Manager 3 In the GeneMapper Manager select the Size Standards tab 4 Verify that the Size Standard tab does not list the missing size standard or that it has not been renamed Symptom Possible Cause Solution SNF PQV displays Check Autoanalysis only The size standard may have been set incorrectly in the plate record of the Data Collection Software Do one of the following e If using an Applied Biosystems size standard click Import to import the definition from the The size standard definition has been default Panels folder renamed deleted or does not exist e Click New to create a custom size standard of the same name GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 23 m Chapter 1 Process Quality Values and Basic Troubleshooting SP Split Peak SP Split Peak Description Function Expected Values Troubleshooting The SP PQV indicates that the peak for the associated genotype is part of a pair of overlapping peaks that are less than 0 25 base pairs apart the horizon
14. than X of the larger peak that is within 1 data point Bg Pass or A Check Select the affected genotype click iiij Analysis Display Plots then review the peak at the appropriate location SQ Sizing Quality Description Function Expected Values The SQ PQV reports the result of the Sizing Quality test which gauges the similarity between the fragment pattern defined by the size standard definition and the actual distribution of size standard peaks in the sample data The metric of the Sizing Quality test is a combination of several values which measure the success of the algorithms that Identify and eliminate primer peaks based on peak shape Perform size matching ratio matching Make a size calling curve using the chosen sizing method The Sizing Quality metric yields a value between 0 and 1 Based on the PQV Threshold settings in the analysis method used to analyze the data the software translates the metric into the gl Pass A Check or Low Quality flags to indicate the result of the test Note The GeneMapper Software does not complete the analysis of samples that fail the Sizing Quality test samples that display I Pass A Check or Low Quality Note When performing size calling using the Classic sizing method the software cannot determine Sizing Quality and therefore SQ is always A Check GeneMapper Software Version 4 1 Reference and Troubleshooting Guide
15. 0 2040 Figure 3 9 Electropherogram showing four resolved peaks detected as two peaks GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Effects of Reducing the Window Size Value While Increasing the Polynomial Degree Value Chapter 3 Algorithms E Optimizing Peak Detection Sensitivity i Figure 3 10 shows the data presented in the figure above re analyzed with a window size value of 10 and polynomial degree value of 5 1820 1930 1840 1850 1860 1870 1880 1890 1900 1910 1920 1990 1940 1950 1960 1970 1980 1990 2000 2010 2020 200 2040 Figure 3 10 Electropherogram showing all four peaks detected after reducing the window size value and increasing the polynomial degree value Example 3 Extreme Settings Effects of Extreme Settings Figure 3 11 shows the result of an analysis using a peak window size value set to 10 and a polynomial degree set to 9 These extreme settings for peak detection caused several peaks to be split and detected as two separate peaks 4010 4020 4030 4040 4050 4060 4070 4080 4090 400 4110 4120 4130 4140 4150 460 4170 480 4190 4200 4210 4220 4230 Figure 3 11 Electropherogram showing the result of an analysis using extreme setting for peak detection GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 43 i Chapter 3 Algorithms Slope Thresholds for Peak Start End Parameters Slope Thr
16. 12 40 PM fsa Start At 1000 Fragment sizes NED yellow8 31 04 1 11 PM fsa Start At 1000 for the size Lr ROX red8 31 04 1 41 PM fsa Start At 1000 standard Points 100000 Matrix Result amp Y R 1 0000 0 8695 0 1770 0 0043 Generated matrix B h 6 jposs04 1 0000 0 8310 0 0237 data v 06902 0 8526 1 0000 0 3741 R oss pz250 ps257 fomo 4 Click Done to close the GeneMapper Manager 5 In the Matrix column of the Samples tab select the new matrix then analyze the project GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 17 Chapter 1 Process Quality Values and Basic Troubleshooting NB Narrow Bin NB Narrow Bin Description Function Expected Values Troubleshooting The NB PQV indicates that the apex of the associated peak for the associated genotype is present within 0 5 base pairs of a bin that does not contain a peak This PQV is designed to capture peaks that are outside of bin boundaries because of incorrect bin definitions Bg Pass or A Check Select the affected genotype click iiij Analysis gt Display Plots then review the peak at the appropriate bin location Symptom Possible Cause Solution NB PQV displays Check You created a bin that is too narrow to contain its associated allele peak In the Panel Manager edit the bin width and or location so that it contains the allele peak
17. A problem with the chemistry is Check primer lengths and A Check causing peaks from two different electrophoresis conditions and markers not to resolve possibly adjust as necessary because either of the primers are too similar in length or the mobilities of the two primer fragments are similar 12 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide GQ Genotype Quality Description Function Chapter 1 Process Quality Values and Basic Troubleshooting wm GQ Genotype Quality The GQ PQV provides a summary of the quality metrics for each genotype The GQ value is a calculated combination of the relevant weighted PQVs and the Marker Quality value for the genotype Calculation of the Genotype Quality GQ Metric The formulas used by the GeneMapper Software to calculate the GQ value are analysis specific and differ largely based on the PQVs supported by each application The following general formula describes the genotype quality calculation GQ MQx 1 BD x 1 OS x x 1 SPU where the Marker Quality MQ value is modified by the user defined PQVs to generate the final GQ value and the PQVs are weighted from 0 to 1 The actual value of each PQV in the equation is 1 minus the weight assigned in the Quality Flags tab of the analysis method used to analyze the data PQV Weight Net Effect on GQ Calculation 0 No effect on the GQ calculation The initial value of 1 minus the weight o
18. Cross Talk 0 000 cee ee 32 SNPlex System Troubleshooting 33 OVerVIeW od dos dtd tie gs sd laste exe P Sesto ee erdt NA ee Sled 34 Algorithms 35 Genotyping Algorithms llle 36 Peak Detection sus oe biker DER PRISE aala 38 Optimizing Peak Detection Sensitivity 41 Example 1 Reducing Window Size sss 41 Example 2 Reducing Window Size Increasing Polynomial Degree M n P 42 Example 3 Extreme Settings 43 Slope Thresholds for Peak Start End Parameters 44 Slope Threshold Example sellers 45 Size Matching Size Calling Algorithm 0 005 46 Size Calling Methods Classic and Advanced Modes 47 Least Squares Method 2c eee ees 47 Cubic Spline Interpolation Method 49 Local Southern Method sees 50 Global Southern Method 00 eee 52 Allele Calling Algorithms lees 53 Microsatellite Analysis Methods 54 SNPlex System Analysis Methods 55 Glossary 59 Index 67 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Preface How to Use This Guide Purpose of This This guide describes the function of the Process Quality Values Guide PQV for the supported analyses of the GeneMapper Software explains the fundamental algorithms used by the software and pr
19. Degree and Peak Window Size Use the Polynomial Degree and the Peak Window Size settings to adjust the sensitivity of the peak detection You can adjust these parameters to detect a single base pair difference while minimizing the detection of shoulder effects or noise Sensitivity increases with larger polynomial degree values and smaller window size values Conversely sensitivity decreases with smaller polynomial degree values and larger window size values How They Work The peak window size functions with the polynomial degree to set the sensitivity of peak detection The peak detector calculates the first derivative of a polynomial curve fitted to the data within a window that is centered on each data point in the analysis range Using curves with larger polynomial degree values allows the curve to more closely approximate the signal and therefore the peak detector captures more peak structure in the electropherogram The peak window size sets the width in data points of the window to which the polynomial curve is fitted to data Higher peak window size values smooth out the polynomial curve which limits the structure being detected Smaller window size values allow a curve to better fit the underlying data How to Use the Peak Detection Parameters Use the table below to adjust the sensitivity of detection Function Polynomial Degree Value Window Size Value Increase sensitivity Higher Lower Decrease sen
20. Figure 3 20 on page 54 shows an example of three different allele Different Allele Calling Algorithms a 1281659 27 calling algorithms for 16 samples User annotations are indicated by the red circles and allele caller outputs are indicated by the green black and blue asterisks Note that consensus between multiple callers virtually ensures that the calls are correct In samples 1 and p the algorithms have not made a call because they determined that the data are too complex to act on Here the blue asterisks show the calls transmitted to the user Low quality values are reported because in both cases the first algorithm did not call and in 1 the black caller does not agree with the blue Despite these conditions however the calls are correct The low quality values alert the user to potential problems such as the spurious peak in 1 and the high background in p b S1597 24 c 88258 24 d S125 5 1068 e 98158 16 1354 282 4 g 158120 24 f S196 24 h 79486 21 1118 i S2382 20 241 488 TC 1388 j XS1073 13 k 128310 7 1 115987 10 1011 m S1271 14 804 1826 353 n 1653103 6 o 119987 3 p 88270 5 1428 Figure 3 20 The effect of three different allele calling algorithms on 16 different samples GeneMapper Software Version 4 1 Reference and Troubl
21. GeneMapper Software Version 4 1 O Applied KS Bibsystems Reference and Troubleshooting Guide Process Quality Values and Basic Troubleshooting SNPlex System Troubleshooting nan Algorithms O Applied GeneMapper Software Version 4 1 KS Biosystems Reference and VFoubleshootng DE Troubleshooting Guide SNPlex System Troubleshooting Algorithms For Research Use Only Not for use in diagnostic procedures Information in this document is subject to change without notice APPLIED BIOSYSTEMS DISCLAIMS ALL WARRANTIES WITH RESPECT TO THIS DOCUMENT EXPRESSED OR IMPLIED INCLUDING BUT NOT LIMITED TO THOSE OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE TO THE FULLEST EXTENT ALLOWED BY LAW IN NO EVENT SHALL APPLIED BIOSYSTEMS BE LIABLE WHETHER IN CONTRACT TORT WARRANTY OR UNDER ANY STATUTE OR ON ANY OTHER BASIS FOR SPECIAL INCIDENTAL INDIRECT PUNITIVE MULTIPLE OR CONSEQUENTIAL DAMAGES IN CONNECTION WITH OR ARISING FROM THIS DOCUMENT INCLUDING BUT NOT LIMITED TO THE USE THEREOF WHETHER OR NOT FORESEEABLE AND WHETHER OR NOT APPLIED BIOSYSTEMS IS ADVISED OF THE POSSIBILITY OF SUCH DAMAGES NOTICE TO PURCHASER DISCLAIMER OF LICENSE Purchase of this software product alone does not imply any license under any process instrument or other apparatus system composition reagent or kit rights under patent claims owned or otherwise controlled by Applied Biosystems either expressly or by estoppel
22. Plex System 66 An Applied Biosystems kit containing reagents used to PCR amplify any SNP markers using single base extension technology Sample files can then be sized and genotyped by using a SNaPshot analysis in the GeneMapper Software Primer extension based chemistry for SNP validation Single Nucleotide Polymorphism A marker consisting of a single base pair that varies thereby creating up to four alleles of the marker In this document SNP refers to SNaPshot system markers and SNPlex systems Single Nucleotide Polymorphism used High throughput assay for genotyping GeneMapper Software Version 4 1 Reference and Troubleshooting Guide A ABB automatic bin builder definition 59 access control list definition 59 admin profile definition 59 admin security group definition 59 admin user group definition 59 admin user definition 59 ADO Allele Display Overflow PQV 8 AE Allele Edit PQV 8 algorithms allele calling 36 53 binning 36 definition 60 overview 36 peak detection 36 size matching 36 all user group definition 60 Allele Display Overflow PQV 8 Allele Edit PQV 8 Allele Number PQV 9 allele calling algorithm 53 definition 60 AN Allele Number PQV 9 analysis method definition 60 Applied Biosystems contacting viii customer feedback on documentation viii Information Development department viii Technical Support viii association definition 61 assumptions for using this guide v
23. Run Information Provides basic information about the configuration of the compatible Applied Biosystems electrophoresis instrument and the run itself Instrument Name Displays the name of the instrument used to run the sample The instrument name is important when diagnosing trends in analysis errors that can be traced back to the instrument used to run the failed sample s Data Collection Ver Displays the version number of the Data Collection Software used to run the sample Data Collection Settings Describes the configuration of the Data Collection Software at the time the sample was run The data displayed in this group can be used to troubleshoot problems caused by modifications to run modules By comparing the Data Collection Settings information from passing and failing runs you can identify any changes made to the run module intentional or unintentional that may have caused or contributed to the failure Note The data displayed in the EPT tab provides a log of the actual parameters throughout the run Capillary Information Provides the basic specifications of the capillary array used to run the sample Capillary Number Displays the number of the capillary used to run the associated sample Like the instrument name the capillary number is important when diagnosing trends in analysis errors that can be traced back to the capillary used to run the failed sample s GeneMapper Software Version 4
24. SP Split Peak v 24 SPA Single Peak Artifact v 24 SPU Spectral Pull Up v v v v v 25 SQ Sizing Quality v v v v v 25 XTLK Cross Talk v v v 8 4 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Common Troubleshooting Procedures About the Procedures Displaying Sample Information Raw and EPT Data Chapter 1 Process Quality Values and Basic Troubleshooting mm Diagnosing and Resolving Basic Problems Procedures commonly used to troubleshoot errors and irregularities in fragment analysis data B Displaying Sample Information Raw and EPT Data see below B Displaying Numeric PQV Metrics aaa 7 1 In the GeneMapper window select the Samples tab 2 In the Navigation Pane of the GeneMapper window a Click to expand the contents of the project folder b From the list of samples select a sample that displayed Check or Low Quality 3 Select the Info Raw Data and EPT Data tabs as needed to display the sample information for the selected file Info tab Displays a summary of all information for the associated sample file see Table 1 2 on page 6 Raw Data tab Displays an electropherogram of spectral data collected during the run of the associated sample The spectral data is displayed in relative fluorescent units RFU EPT Data tab Displays the EPT electrical power and temperature data for the associated sample throughout the course of the run
25. ailable from the software support section of the Applied Biosystems website www appliedbiosystems com support software 1 Click Tools GeneMapper Manager 2 In the GeneMapper Manager select the Matrices tab then click Import 3 In the Importing Matrix dialog box navigate to and select the matrix file then click Import 4 Click Done to close the GeneMapper Manager then analyze the project GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 1 Process Quality Values and Basic Troubleshooting wa MNF Matrix Not Found Generatinga 1 Click Tools GeneMapper Manager Matrix 2 In the GeneMapper Manager select the Matrices tab then click New 3 In the Matrix Editor dialog box a Type a name and description for the matrix b In the Number of Dyes drop down list select the number of dyes present in the matrix 4 or 5 c Click B navigate to and select the sample file for the blue matrix standard then click Open d Repeat step 3c for the remaining dyes in the matrix Green Yellow Red and Orange if applicable e Click Create to create the matrix f Click OK G5 Matrix Editor r Matrix Description Matrix Name Example Matrix qy Size standard Description name r Matrix Settings Select the Matrix Standard Sample File Number of Dyes 4v Size standard dye FAM blue8 31 04 11 52 AM fsa Startat 1000 channel JOE green8 31 04
26. alysis method go to step 4 Create a copy of the current analysis method a Select the current analysis method b Click Save As c In the Save As dialog box type a name for the new method then click OK 4 Select the analysis method you want to modify then click Open 5 In the Analysis Method Editor select the Peak Detector tab 6 Modify the appropriate Peak Amplitude Threshold settings as needed Ideally you should set the threshold of the appropriate dye channel to a value less than the signal intensity of the shortest size standard peak Note Applied Biosystems recommends using Peak Amplitude Threshold settings of no less than 50 RFU r Peak Detection VR E Peak Amplitude Thresholds CE B fo R fo Bp Peak Amplitude Baseine Window FT pes G fo p 50 soe Caro Method Threshold settings Y so O 50 a coat 7 Click OK to save the analysis method 8 Click Done to close the GeneMapper Manager then reanalyze the samples using the new analysis method 30 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 1 Process Quality Values and Basic Troubleshooting wu SQ Sizing Quality Customizing a You can create a custom size standard definition to correct some Size Standard problems that consistently cause samples to fail sizing Examples of Definition problems that you can resolve using custom size standards include a series of samples that fail sizing b
27. as performed using a polynomial degree of 3 and a peak window size of 19 data points 4140 4150 4160 4170 4190 4100 4200 4210 4220 4230 4240 4250 4200 4270 4280 4200 4300 4310 4320 4030 4940 4050 4300 4370 4000 4090 4400 4410 4420 2500 JU ir dr AG tr d M d vit ar us r a uv i Figure 3 7 Electropherogram showing two resolved alleles detected as a single peak Note For information on the tick marks displayed in the electropherogram see Examining Peak Definitions on page 39 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 41 Wu Chapter 3 Algorithms n Optimizing Peak Detection Sensitivity Effects of Reducing Figure 3 8 shows that both alleles are detected after reanalyzing with the polynomial degree set to 3 and the window size value decreased to 15 from 19 data points A U AWU AU AU AU FU ALW ZZU AEU 4 40 AOU AU FV GRU AD RUU KW KU AID AYAW KW KO 4S U Y RU GAW AU aa 2500 Figure 3 8 Electropherogram showing the alleles detected as two peaks after decreasing the window size value Example 2 Reducing Window Size Increasing Polynomial Degree Initial Electropherogram 42 Figure 3 9 shows an analysis performed using a polynomial degree of 3 and a peak window size of 19 data points 1840 1950 1890 1870 1880 1890 1900 1910 1920 1930 1940 1050 1960 1970 1980 1990 2000 2010 2020 202
28. ced Modes In the Local Southern method The fitting constants of the curve are calculated for each group of three neighboring points on the standard A separate curve is created for each set of three points Acurve is then created by using three standard points two points below and one point above the fragment then a fragment size is determined Another curve is created by looking at an additional set of three points one point below and two points above the fragment then another value is assigned The two size values are averaged to determine the unknown fragment length GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 51 mrs Chapter 3 Algorithms R Size Calling Methods Classic and Advanced Modes Global Southern Method Overview Global Southern Method Equations How the Global Southern Method Works 52 This method is similar to the Least Squares method in that it compensates for standard fragments that may run anomalously The method creates a best fit line through all the available points and then uses values on that line to calculate the fragment values Best Fit 2nd Order Curve AO 9 771623E 01 Al 1 220838E 01 A2 4 856885E 06 R2 1 000 Size Calling Curve Global Southern Method T T T T T T T T 1000 1500 2000 2500 3000 3500 4000 4500 Figure 3 19 Global Southern Method Equation Description Attempts to describe the reciprocal relationship be
29. custom standard b Select Size Standard Dye gt appropriate dye gt c In the Size Standard table type the size values for the size standard press Enter after typing each value IMPORTANT After typing the last value you must press Enter to ensure the final value is included in the definition IMPORTANT The values for the Analysis Range and Sizing Range defined in the Allele and Peak Detector tabs of the analysis method must match the peak range defined by the associated size standard 6 Click OK to save the size standard T Click Done to close the GeneMapper Manager 8 In the Samples tab of the GeneMapper window apply the new size standard to the samples of the project 9 Reanalyze the sample using the new setting to verify that the problem has been resolved XTLK Cross Talk Description Function Expected Values 32 The XTLK PQV indicates that at the peak position of the associated genotype the ratio of the signals collected from the neighboring capillaries exceed the Cross talk ratio setting in the Peak Quality tab of the analysis method Note When the XTLK PQV is triggered the software reduces the GQ PQV by 50 the default multiplier is 0 5 J Pass or A Check GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 2 SNPlex System Troubleshooting In this chapter Process Quality Values M Overview RR and Basic Troubleshooting
30. d for Peak closer to its apex Start value from zero to a positive number End point of a peak Change the Slope Threshold for Peak closer to its apex End value to a more negative number GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 3 Algorithms wu Slope Threshold Example Slope Threshold Example Asymmetrical Peak Initial Electropherogram The initial analysis using a value of 0 for both the Slope Threshold for Peak Start and the Slope Threshold for Peak End produces an asymmetrical peak with a noticeable tail on the right side 2120 2190 2140 2150 2160 2170 2180 2190 2200 2210 2220 22 0 224 2250 2200 2270 2290 229 2 00 2010 VW 1600 1 o 1 Figure 3 12 Electropherogram showing an asymmetrical peak Adjusting Slope Threshold for Peak End After reanalyzing with a value of 35 0 for the Slope Threshold for Peak End the end point that defines the peak moves closer to its apex thereby removing the tailing feature Note that the only change to tabular data is the area peak size and height are unchanged 2120 2190 2140 2150 2160 2170 2190 2190 2200 2210 2220 2220 2240 2250 2280 2270 2290 2290 2 00 NO NW Figure 3 13 Electropherogram showing the effect of changing the slope threshold for peak end GeneMapper Software Versi
31. duce a sizing curve based on Multiple Linear Regression Advantages Figures 3 15 and 3 16 on page 48 show that in nearly all instances the mobility of an individual DNA fragment is coincident with the best curve fit of the entire data set Stated differently the mobility of most DNA fragments is strictly length dependent This method automatically compensates for fragments that run anomalously The GeneMapper Software calculates a best fit least squares curve for all samples regardless of the size calling method you choose The curve is black in the Standard Sizing Curve window Note The graphs in this section were generated using Version 3 5 1 of the GeneScan Software The results are similar to those obtained when you use the GeneMapper Software GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 47 Chapter 3 Algorithms Size Calling Methods Classic and Advanced Modes Best Fit 2nd Order Curve aso A0 9 771623E 01 Al 1 220838E 01 A2 4 856885E 06 300 R 2 1 000 250 200 150 100 50 Size Calling Curve 0 2nd Order Least Squares T T T T T T T T 1000 1500 2000 2500 3000 3500 4000 4500 Figure 3 15 2nd Order Least Squares size calling curve Best Fit 3rd Order Curve 350 AO 1 266506E 02 T Al 1 608974E 01 A2 2 041442E 05 200 3 1 887158E 09 R 2 1 000 250 200 150 100 Size Calling Curve 0 3rd Order Least Squares T T T T
32. e the Size Match Editor to correct peaks that are miscalled Miscalled Peaks by the GeneMapper Software To correct a miscalled size standard 1 In the Navigation Pane of the Size Match Editor select the sample file containing the miscalled peak Remove the label from the miscalled peak a Select the peak with the label by clicking inside the body of the peak b Select Edit Delete Size Label or right click the peak then select Delete Apply the label to the correct peak a Select the correct peak b Select Edit Add Size Label or right click the peak then select Add c In the Select Size dialog box double click the label to apply to the selected peak Click amp Tools gt Check Sizing Quality to verify that the sample sizes correctly Click Apply to save the changes then click OK IMPORTANT You must click Apply to reanalyze the sample Note Observe that the cell in the Status column for the sample now displays 5 Analysis Required Reanalyze the sample using the new setting to verify that the problem is resolved 28 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 1 Process Quality Values and Basic Troubleshooting wH SQ Sizing Quality Adjusting Peak You can resolve a significant number of sizing failures by adjusting Detection the peak detection thresholds of the analysis method for a project Thresholds The software identifies peak
33. ecause The primer peak prevents the software from detecting and sizing the peaks of the smaller size standard fragments 0 1000 2000 3000 4000 5000 6000 Primer peak Size standard peaks eclipsed by the primer peak A fragment of a custom size standard does not migrate as expected during electrophoresis About GeneMapper Software Size Standards Before the GeneMapper Software can size fragment analysis data it must contain information about the size standard that was run with the samples The size standard definition supplies the software with two pieces of information the color of the dye associated with the size standard and the sizes in bp of the fragments that comprise the size standard Although the software provides definitions for all Applied Biosystems size standards you may need to create your own definition if you choose to use a third party standard or experience difficulty analyzing your data To create a custom size standard definition 1 In the GeneMapper window click Tools GeneMapper Manager 2 In the GeneMapper Manager select the Size Standards tab 3 Click New 4 In the Select Dye and Analysis Method dialog box select Basic or Advanced then click OK GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 31 Chapter 1 Process Quality Values and Basic Troubleshooting XTLK Cross Talk 5 In the Size Standard Editor a In the Name field type a name for the
34. elic ladder in the Samples tab You generated bins using the Auto Bin View the allele peak s for the marker function but the GQ value for a marker in the Genotypes Plot window was less than the Minimum Quality Determine if the allele peaks s are Value of 0 1 as set in the Auto Bin valid If so manually create bin s for dialog box the peak s 10 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide CC Control Concordance Description Function Expected Values Troubleshooting Chapter 1 Process Quality Values and Basic Troubleshooting wm CC Control Concordance The CC PQV indicates that the associated control sample does not exactly match the defined alleles for the related marker IMPORTANT Applied Biosystems recommends running the control sample at least once for every panel Note The CC PQV serves primarily as an internal control for quality assurance Bg Pass or A Check Select the affected control sample in the Samples tab of the GeneMapper window click iij Analysis gt Display Plots then review the positions of the peaks relative to the bins Symptom Possible Cause Solution CC PQV displays Check The allele calls of the sample defined Run the correct positive control and as the Positive Control in the Samples add the sample file to the project tab do not match the Positive Control then define the sample as the allele calls in the marker defin
35. en select the Analysis tab 2 In the Quality Metrics Display settings select Numbers Quality Metrics Display C Symbols 3 Click OK to apply the settings GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 7 m Chapter 1 Process Quality Values and Basic Troubleshooting ADO Allele Display Overflow ADO Allele Display Overflow Description Function Expected Values Troubleshooting The ADO PQV indicates that the number of alleles called for the associated sample at the specified marker exceeds the Allele Setting in the Genoptypes tab of the table setting Because the software is configured to display fewer alleles than are present the data for the additional allele is hidden from view Note For each allele detected by the software the Genotypes tab displays six columns name size height area mutation and comments gt Indicates that the associated sample contains a number of alleles at the specified marker that is greater than the user defined limit Select the affected genotype click iiij Analysis Display Plots then review the affected sample for miscalled peaks AE Allele Edit Description The AE PQV indicates whether or not a user modified the allele call Function Expected Values for the associated genotype Note Allele calls can be modified in the Samples Plot the Genotypes Plot and the Cluster Plot X Indicates that the associated genot
36. esholds for Peak Start End Parameters About These Parameters How These Parameters Work Guidelines for Using These Parameters Using These Parameters 44 Use the Slope Threshold for Peak Start and Slope Threshold for Peak End parameters to adjust the start and end points of a peak The values assigned to these parameters can be used to better position the start and end points of an asymmetrical peak or a poorly resolved shouldering peak to more accurately reflect the peak position and area In general from left to right the slope of a peak increases from the baseline up to the apex From the apex down to the baseline the slope becomes decreasingly negative until it returns to zero at the baseline Apex Increasingly Increasingly positive slope N negative slope D 0 0 Baseline If either of the slope values you enter exceeds the slope of the peak being detected the software overrides your value and reverts to zero For typical or symmetrical peaks use a value of zero For asymmetrical peaks select values other than zero to better reflect the beginning and end points A value of zero does not affect the sizing accuracy or precision of an asymmetrical peak Note The size of a detected peak is the calculated apex between the start and end points of a peak and does not change based on your settings To move the Then Example Start point of apeak Change the Slope Threshol
37. eshooting Guide Chapter 3 Algorithms wu Allele Calling Algorithms SNPlex System Analysis Methods Overview Rules Genotyping Algorithm The GeneMapper Software provides the following allele calling methods for SNPlex System Analysis B Rules Genotyping Algorithm see below B Model Genotyping Algorithm 2 0005 56 The Rules Maximum Likelihood or ML SNP genotyping algorithm consists of several processes genotype cluster identification sample cluster classification and confidence value assignment Input Allele 1 and 2 Intensities Y Allele Intensity Correction CA Resampling Estimate Model Parameters Exclude Outliers e Number of clusters e Mean variance of each cluster i e Allele frequency Detect Outliers Y Compute Most Likely Genotype T j and Confidence Values First Pass Outlier probability Y Output Genotypes and Quality Values for all Points Second Pass and Plate Score optional Y Filter Samples and Recluster Second Pass Y C Done Figure 3 21 Rules algorithm block diagram Because of small systematic differences in assay performance between the alleles in a SNP fixed classification boundaries between the clusters do not give accurate results for most if not all current SNP detection platforms Clustering like all calibration methods can correct for systematic errors but not random err
38. f O yields a PQV of 1 When used in the GQ calculation the PQV has no effect since 1 MQ MQ 1 Reduces the GQ value to 0 The initial value of 1 minus the weight of 1 yields a PQV of 0 When used in the GQ calculation the PQV automatically causes the GQ to fail since 0 MQ 0 0 x 1 Reduces the GQ value to the fraction specified by the weight The higher the value the greater the effect on GQ IMPORTANT The filtering of individual PQVs is controlled by the threshold settings in the Peak Quality tab of the analysis method Also the PQVs remain fully functional regardless of the weights used GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 13 m Chapter 1 Process Quality Values and Basic Troubleshooting GQ Genotype Quality Expected Values Troubleshooting 14 Calculation of the Marker Quality Metric Figure 1 1 shows how the GeneMapper Software generates a Marker Quality MQ value from sample peak data with assigned Allele Quality AQ values AQ values are a function of quality value assignments for sizing quality allele calling quality bin assignment quality and bin quality Note When analyzing SNPlex System sample data the GeneMapper Software calculates GQ values depending on the method Model or Rules selected to perform allele calling The following figure illustrates the derivation of GQ values using the Rules method AQ JL m4 AQ JL Jn GR2119 F
39. fragments used in subsequent runs OVL Overlap Description The OVL PQV indicates that the peak for the associated genotype Function has been called twice by the GeneMapper Software If the ranges of two bins overlap a peak can reside in both bins and therefore be called twice once for each allele Expected Values Pass or A Check Troubleshooting Select the affected genotype click jij Analysis gt Display Plots then review the peak and associated bins at the appropriate location 20 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 1 Process Quality Values and Basic Troubleshooting S PHR Peak Height Ratio PHR Peak Height Ratio Description The PHR PQV indicates that the apex of the peak for the associated Function genotype is Present at a position within 1 base pair of another peak and The ratio of the height of the lower peak to that of the higher peak is less than the Minimum Peak Height Ratio setting in the Peak Quality tab of the analysis method Note For LMS markers the ratio is calculated based on the peak heights of the called allele peaks Note For SNaPshot kit analysis the ratios are calculated as they are for microsatellite markers except that they span two different colors and only two peaks are used in the calculation Expected Values Pass or A Check Troubleshooting Select the affected genotype click ij Analy
40. ftware Version 4 1 Reference and Troubleshooting Guide vii Preface How to Obtain Support Send Us Your Comments Obtaining Information from the Online Help Applied Biosystems welcomes your comments and suggestions for improving its user documents You can e mail your comments to techpubs appliedbiosystems com The GeneMapper Software features an online help system that describes how to use each feature of the user interface To access the online help click in any window or dialog box Help Contents and Index if available for more information How to Obtain Support viii For the latest services and support information for all locations go to http www appliedbiosystems com then click the link for Support At the Support page you can e e e e e e Search through frequently asked questions FAQs Submit a question directly to Technical Support Order Applied Biosystems user documents MSDSs certificates of analysis and other related documents Download PDF documents Obtain information about customer training Download software updates and patches In addition the Support page provides access to worldwide telephone and fax numbers to contact Applied Biosystems Technical Support and Sales facilities GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Process Quality Values and Basic Troubleshooting In this chapter Process Quality Values E Diagnosing and Resolvi
41. he Info tab then note the name Sample File and location Sample Origin Path of the sample Symptom Possible Cause Solution SFNF PQV displays Check Sample file has been renamed moved or deleted Search the local drives of the computer for the sample file then do one of the following e f you cannot find the file no further action can be taken to resolve the PQV flag e f you find the file use the Associate Sample feature to direct the software to the new location as follows a In the Samples tab of the GeneMapper window select the affected samples b Select File Associate Samples c In the Select Folder dialog box select the folder containing the missing files then click Select 22 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide SHP Sharp Peak Description Function Expected Values Troubleshooting Chapter 1 Process Quality Values and Basic Troubleshooting SHP Sharp Peak The SHP PQV indicates that the peak for the associated genotype is part of a cluster of peaks with a large narrow peak in the middle whose width is 50 less than either of the neighboring peaks J Pass or A Check Select the affected genotype click iiij Analysis Display Plots then review the peak at the appropriate location SNF Size Standard Not Found Description Function Expected Values Troubleshooting The SNF PQV indicates whether or
42. he basis for troubleshooting fragment analysis data using the software PQVs are application specific metrics where each evaluates the data for a specific quality that is consistent with a problem associated with the type of analysis In this way the PQV system can alert you to potential problems and provide you with a starting point for investigation Each individual PQV displays the result of a unique algorithmic test that evaluates a specific property of the fragment analysis data The software performs the PQV tests in a specific sequence during the analysis With the exception of the Sizing Quality SQ PQV the software completes the analysis of each sample in a project even if a sample fails one or more PQV tests The majority of PQV metrics yield numeric values between 0 and 1 where indicates that the related sample data or genotype completely passed the associated test Following the analysis the software uses the upper and lower thresholds for each PQV to translate the numeric score into one of three symbols displayed in the Samples or Genotypes tab of the GeneMapper window Note Ifthe thresholds of a PQV can be customized the software displays the parameters in the Quality Flags tab of the analysis method Default Symbol Definition Range Pass The sample or genotype passed the PQV 0 75 to 1 0 test Check A possible problem exists for the sample 0 25 to 0 75 or genotype o Low Quality Fail T
43. here is a strong possibility that 0 0 to 0 25 a problem exists for the sample or genotype Note Applied Biosystems recommends examining all samples that produce j Check or Low Quality GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 1 Process Quality Values and Basic Troubleshooting WM Diagnosing and Resolving Basic Problems Note The Allele Display Overflow ADO and Allele Edit AE PQVs of the Genotypes tab report their results as 2X instead of the colored icons Note The GeneMapper Software does not complete the analysis of samples that display Low Quality for the Sizing Quality SQ PQV test Adjusting the Threshold Settings of PQV Tests Some PQV metrics include components that can be customized In those cases the user defined parameters for the PQV appear in the Peak Quality tab of analysis methods for the applicable analysis type Adjusting the Weights of PQV The majority of PQV contribute to the Genotype Quality GQ PQV a metric used to gauge the confidence of each genotype call In those cases some PQV contain user defined weights that determine how significantly the associated PQV affect the GQ PQV calculation For those PQV the user defined weights appear in the Quality Flag tab of analysis methods for the applicable analysis type For more information on the calculation of the GQ PQV see GQ Genotype Quality on page 13 N
44. igure 1 1 Calculation of the Marker Quality metric x AQ MQ Jg Pass A Check or amp Fail Note The software assigns the GQ PQV flags based on the POV threshold settings in the Quality Flags tab of the analysis method Review the PQV for the affected genotype to determine the metric that is causing the GQ PQV to fail Note To better determine how individual PQV contribute to the GQ PQV configure the software to display the PQV numerically as explained in Displaying Numeric PQV Metrics on page 7 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 1 Process Quality Values and Basic Troubleshooting wu LPH Low Peak Height LPH Low Peak Height Description The LPH PQV indicates that the height of the peak for the associated Function genotype is lower than the associated heterozygous or homozygous height limit that is specified in the analysis method You can set homozygous value default is 200 and heterozygous value default is 100 in the Peak Quality tab of the analysis method Note When the LPH PQV is triggered the software reduces the GQ PQV by 50 the default multiplier is 0 5 Expected Values Pass or A Check Troubleshooting Select the affected genotype click ij Analysis gt Display Plots then review the associated peak for irregularities GeneMapper Software Version 4 1 Reference and Troubleshooti
45. ing an exiting one An object associated with an object type used to tell the audit component how to audit an object type A collection of data defined by an application Also referred to as an object The description of a single audit event A feature that uses reference data generated by the Primer Focus kit to quickly define new SNP markers and bin sets Within the GeneMapper Software a fragment size bp or basepair range and dye color that define an allele within a marker You create a bin for each possible allele associated with a marker Within the GeneMapper Software a collection of bins allele definitions typically specific to a set of experimental conditions usually an instrument Bin sets are available inside a kit An in memory representation of the access control data The Admin Tool modifies the data in the cache When the Admin Tool or Admin API issues the save command the data in the cache are written to the data store A term from user authentication indicating that the user is asked to provide identification typically by entering a password A long stretch of coiled strands of DNA and proteins containing many genes Human DNA is contained within 23 pairs of chromosomes GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 61 Glossary control Control Security Group data access control Data group data rights database diploid dye set electropherogram elec
46. ition Positive Control in the Samples tab because the well contains the of the GeneMapper window incorrect positive control sample The allele calls of the sample defined Edit the Positive Control allele calls in as the Positive Control in the Samples the marker definition in the Panel tab do not match the positive control Manager allele calls in the marker definition because the alleles were defined incorrectly The sample defined as the Negative Rerun the negative control and add Control contains an allele peak due the sample file to the project then to the presence of a spike caused by define the sample as the Negative dust or a gas bubble Control in the Samples tab of the GeneMapper window GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 11 m Chapter 1 Process Quality Values and Basic Troubleshooting DP Double Peak DP Double Peak Description The DP PQV indicates that the peak for the associated genotype Function Resides in a bin with another peak of the same dye color and Theratio ofthe peak height minor major peak height is greater than the Double Peak setting in the Peak Quality tab of the Analysis Method Expected Values Pass or A Check Troubleshooting Select the affected genotype click i Analysis gt Display Plots then review the sample data at the appropriate bin for additional peaks Symptom Possible Cause Solution DP PQV displays
47. k See Adjusting Peak Detection Thresholds on page 29 for more information SQ PW displays or Size standard peaks occur within a primer peak Insufficient cleanup step Create and analyze the data using a custom size standard that does not include the undetectable peak See Correcting Miscalled Peaks on page 28 for more information SQ PW displays or Size standard peaks are clear and distinguishable but consistently have low signal strength Incorrect concentration of size standard in sample loading reagent Increase the concentration of size standard added to subsequent runs Incorrect injection settings Review the injection settings of the run module for errors SQ PW displays or Peaks are clear and distinguishable but have low signal strength Sizing failures occur in a regular pattern the same wells fail repeatedly Electrophoresis or pipetting error Defective capillaries arrays SQ PW displays or Size calling errors occur for different samples on the same capillary over multiple runs Defective capillary See the user manual for your Applied Biosystems electrophoresis instrument for information on troubleshooting defective capillaries arrays GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 27 wm Chapter 1 Process Quality Values and Basic Troubleshooting SQ Sizing Quality Correcting You can us
48. le Calling Algorithms Final Genotyping and Confidence Value Assignment The confidence value is a quantitative estimate of the probability of a correct call that is one minus the estimated error rate This single parameter can be used to optimize the trade off between throughput and accuracy for a particular data set as shown in Figure 3 24 Fraction Accuracy 96 Net Accuracy 96 0 65 0 70 0 75 0 80 0 85 0 90 0 95 1 0 Quality Value Figure 3 24 Accuracy throughput trade off GeneMapper Software Version 4 1 Reference and Troubleshooting Guide ABB Automatic Bin Builder access control list Admin profile Admin security group Admin User Group Administrator User AFLP Glossary The first step in accurate allele assignment After sample files are collected bins are created by the ABB based on the chosen panel information and successive allele calls from sample file collection As each sample file in the collection is processed the bin definitions are refined to reflect the actual data See Security Group A pre configured profile that cannot be removed and that has execute access to all functions Initially associated with the admin user A user must always have an assigned profile A pre configured security group that cannot be removed This security group has been granted all rights to all data to provide a way for at least one user to have admin access to all data A pre config
49. n page 27 to determine an appropriate corrective action Repeat steps 4 through 6 for each sample file 26 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 1 Process Quality Values and Basic Troubleshooting SQ Sizing Quality Table 1 4 SQ PQV Troubleshooting Symptom Possible Cause Solution SQ PW displays or Size Match Editor does not display peak data The Size Standard Dye setting for the size standard definition is not Set to the correct dye SQ PW displays or Peaks do not contain size labels The fragment sizes of the size standard definition do not match the positions of the detected peaks 1 Verify that the correct size standard definition is in use 2 Open the size standard definition and verify that the Dye setting is set to the correct dye Fragment sizes are correct 3 Modify the size standard definition as necessary SQ PW displays or One or more miscalled peaks Peak detection threshold associated with the size standard is set too high or low SQ PQV displays or Peaks are clear and distinguishable but have low signal strength Peak detection threshold associated with the size standard is set too high or low Electrophoresis or pipetting error Adjust the analysis method so that the peak detection threshol d associated with the size standard is greater than the height of the miscalled pea
50. ng Basic Problems 2 Basic nabati B ADO Allele Display Overflow a 8 B AE Allele Edit 0 0 eee 8 B AN Allele Number 0 00 00000 esee 9 B BD Broad Peak wince eol ERE REX UR RAS 9 B BIN Out of Bin Allele 0 2 2 00 205 10 m B CC Control Concordance a 11 B DP Double Peak esee 12 Tio B GQ Genotype Quality eee eee 13 B LPH Low Peak Height aaa 15 B MNF Matrix Not Found an 16 B NB Narrow Bin 18 B OBA One Basepair Allele 2 0 0 0 000 a 18 B OS Offscale 0 2 eI 19 B OVL Overlap Ih 20 E PHR Peak Height Ratio a 21 B SFNF Sample File Not Found 005 22 B SHP Sharp Peak 22 0 23 B SNF Size Standard Not Found 005 23 B SP Split Peak 0 a 24 B SPA Single Peak Artifact an 24 B SPU Spectral Pull Up 0 000202 eee eee 25 B SQ Sizing Quality sse 25 B XTLK Cross Talk sese 32 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 1 m Chapter 1 Process Quality Values and Basic Troubleshooting Diagnosing and Resolving Basic Problems Diagnosing and Resolving Basic Problems PQVs and the Troubleshooting Process About Process Quality Values PQVs The GeneMapper Software has a system of Process Quality Values PQVs that provide t
51. ng Guide 15 wm Chapter 1 Process Quality Values and Basic Troubleshooting MNF Matrix Not Found MNF Matrix Not Found Description Function Expected Values Troubleshooting The MNF PQV indicates whether or not the GeneMapper Software can access the matrix specified in the Matrix column of the Samples tab for the associated sample IMPORTANT Because recent models of Applied Biosystems instruments save matrix data to the sample files they create the MNF flag is applicable only to sample files created by the ABI PRISM 310 and 377 instruments J Pass or A Check Determine the name of the missing matrix file s by reviewing the sample information for the affected samples as explained in Displaying Sample Information Raw and EPT Data on page 5 Symptom Possible Cause Solution MNF PQV displays Check The software could not Locate the missing matrix file then import it as access the matrix file explained in Importing a Matrix File Windows specified in the Matrix Only on page 16 column for the associated sample file If the sample files for the matrix standards used to create the missing matrix are available re create the matrix as explained in Generating a Matrix on page 17 Importing a Matrix File Windows Only 16 IMPORTANT You must convert matrix files created by Macintosh computers before importing them The conversion utility is free and av
52. ns Within the GeneMapper Software a collection of sample files and the analysis parameters for genotyping them See analysis parameters on page 60 Parameters set by the user to prepare a project for analysis All or a subset of the actual samples The reference samples typically contain all of the alleles present in the sample set and are used to create a bin for each allele within the GeneMapper Software Properties that define whether a user has access to data or a function An identifier that can be associated with a user group to confer a set of data rights The universal identifier of the security group and the preferred name of the column in an application table that holds the security group ID Sample File Not Found Automatic audit record creation without prompting of the user A window in GeneMapper Software that allows users to examine size standard electropherograms edit the identification of size standard peaks and view the size calling curve A collection of DNA fragments of known lengths within a range for example 50 to 400 bp all tagged with the same dye The size standard is co injected into the genetic analyzer capillary with the sample then used to size the sample data All Applied Biosystems size standards are labeled with a red or orange dye GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 65 Glossary SNaPshot kit SNaPshot System Multiplex Analysis SNP SNP SN
53. o Obtain More Information een vi How to Obtain Support sasaaa eee viii Process Quality Values and Basic Troubleshooting 1 Diagnosing and Resolving Basic Problems 2 Common Troubleshooting Procedures 5 ADO Allele Display Overflow 0002 0c eee eee 8 AE Allele Edit mer tbe DiGes B4 RE Re Ey X RR ea 8 AN Allele Number IRI 9 BD Broad Peak 000 cece ees 9 BIN Out of Bin Allele llle 10 CC Control Concordance a 11 DP Double Peak 000 ee 12 GQ Genotype Quality llli 13 LPH Low Peak Height 00 00 eee eee eee eee 15 MNF Matrix Not Found 16 NB Narrow Bin spc sertesa inatt eee 18 OBA One Basepair Allele 0 18 OS OMscale 2 edat tee Ge whe ee a ee ant 19 OVE Overlap 242 bhai BAKA DRAG ea bee ede EG ERRSXENES 20 PHR Peak Height Ratio eee eee eee eee 21 SFNF Sample File Not Found eee eeee 22 SHP Sharp P k Xa ei gage een shade TEE ER Lae S 23 SNF Size Standard Not Found eee eee 23 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide iii Contents SP SpliEPealk taga yr esed a a ha eit a eet ye dits 24 SPA Single Peak Artifact 0 0000 c eee eee 24 SPU Spectral Pull Up 0 00 00 eee ee 25 SQ Sizing Quality Na aika maan cs bales eet cea ee eRe oe we ek 25 XTLK
54. oftware you select a Sample Type of Positive Control for the sample file generated from the positive control Additionally in the Panel Manager you define the positive control when creating markers A single stranded piece of DNA or RNA that anneals to a complementary section of DNA and serves as a starting point for chain extension by DNA polymerase Within the GeneMapper Software you select a Sample Type of Primer Focus for the sample file generated using the Primer Focus kit The GeneMapper Software uses this file to automatically create bins using the Auto Panel feature An Applied Biosystems kit containing reagents used to create and amplify all four possible alleles of any SNP marker The kit allows you to take advantage of the Auto Panel feature in the GeneMapper Software to automatically create bins for each allele in a SNaPshot kit analysis GeneMapper Software Version 4 1 Reference and Troubleshooting Guide probe profile project project settings project settings reference samples rights Security group Security ID SFNF silent auditing size match editor size standard Glossary A DNA or RNA fragment that has been labeled in some way for example fluorescent or radioactive then used in a molecular hybridization assay to identify DNA or RNA sequences that are the same or closely related to it in sequence An identifier that gives an administrator the ability to grant or revoke access to functio
55. on 4 1 Reference and Troubleshooting Guide 45 tales Chapter 3 Algorithms Size Matching Size Calling Algorithm Size Matching Size Calling Algorithm Size Matching This algorithm uses a dynamic programming approach that is Size Calling efficient runs in low polynomial time and space and guarantees an Algorithm optimal solution It first matches a list of peaks from the electropherogram to a list of fragment sizes from the size standard It then derives quality values statistically by examining the similarity between the theoretical and actual distance between the fragments Size Matching Figure 3 14 shows an example of how the size matching calling Algorithm algorithm works using contaminated GeneScan 120 size standard Example data Detected peaks standard and contamination are indicated by blue lower bars along the x axis The size standard fragments as determined by the algorithm and their corresponding lengths in base pairs are designated by the upper green bars Note that there are more peaks than size standard locations because the standard was purposely contaminated to test the algorithm The algorithm correctly identifies all the size standard peaks and removes the contamination peaks indicated by the black triangles from consideration The large peak is excluded from the candidate list by a filter that identifies the peak as being atypical with respect to the other peaks Bars indicate size standard determined by algorithm
56. ors Random errors are estimated and assigned appropriate p values GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 55 Chapter 3 Algorithms Allele Calling Algorithms Model Genotyping 56 Algorithm Genotype Cluster Identification The cluster identification stage uses a priori knowledge of the intensity relationship and frequency distribution of SNP alleles to construct a most likely model of the SNP assays generating in a given data set Systematic parameters such as allele assay strength and other chemistry factors are estimated If the most likely model fit is too poor the entire data set is rejected Sample Cluster Classification and Confidence Value Assignment The best fit model is used to classify each point into its most likely class or genotype The likelihood of class membership genotype can be derived in the same step Post Processing Filtering and Reiterations Genotype and sample filters bootstrap resampling and several reiterations are used to ensure the accuracy of the model fit and classifications The Model genotyping algorithm consists of several processes normalization allele calling and confidence value generation Normalization Normalization is a run based algorithm that removes systematic variation of peak height by allele and sample This process is accomplished by comparing the data to a model in which the sum of allele peaks for any given sample is a constant Over man
57. ote You can configure a PQV so that it does not contribute to the GQ by setting the weight to 0 However the PQV remains active Rules for PQV Columns e Quality metrics with Pass A Check values and no Low Quality values are warning flags Analysis does not stop if problems are detected with these properties but Applied Biosystems recommends examining results flagged as A Check Holding the cursor over a column header displays a tooltip that lists the full name of the column the default names are often acronyms GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 3 mda Chapter 1 Process Quality Values and Basic Troubleshooting Diagnosing and Resolving Basic Problems Table 1 1 PQV values by application Analysis PQVs E Microsatelite 3 See Page Abb Name ui A E 3 5 pr ET A min N o ox Do ADO Allele Display Overflow v v v v v 8 AE Allele Edit v v v v v 8 AN Allele Number v v v v 9 BD Broad Peak v v v v v 9 BIN Out of Bin Allele v v v 10 CC Control Concordance v v v v 11 DP Double Peak v 12 GQ Genotype Quality v v v v v 18 LPH Low Peak Height v v v v 15 MNF Matrix Not Found v v v v 16 NB Narrow Bin v 18 OBA One Basepair Allele v 18 OS Off Scale v v v v v 19 PHR Peak Height Ratio v v v v 21 SFNF Sample File Not Found v v v v v 22 SHP Sharp Peak v 23 SNF Size Standard Not Found v v v v v 23
58. ovides basic troubleshooting techniques Audience This guide is intended for trained laboratory personnel Applied Biosystems is not liable for damage or injury that results from use of this guide by unauthorized or untrained parties Assumptions This guide assumes that You have installed GeneMapper Software Version 4 1 as described in the GeneMapper Software Version 4 1 Installation and Administration Guide PN 4403614 You have a working knowledge of the Microsoft Windows operating system Text Conventions This guide uses the following conventions Bold indicates user action For example Type 0 then press Enter for each of the remaining fields Italic text indicates new or important words and is also used for emphasis For example Before analyzing always prepare fresh matrix A right arrow bracket gt separates successive commands you select from a drop down or shortcut menu For example Select File gt Open gt Spot Set Right click the sample row then select View Filter gt View All GeneMapper Software Version 4 1 Reference and Troubleshooting Guide V Preface How to Obtain More Information User Attention Two user attention words appear in Applied Biosystems user Words documentation Each word implies a particular level of observation or action as described below Note Provides information that may be of interest or help but is not critical to the use of the product IMPORTANT
59. owing peaks detected with three different polynomial degrees GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 39 msa Chapter 3 Algorithms Peak Detection Effects of In Figure 3 6 both polynomial curves have a degree of 3 and the Increasing the window size value was increased from 15 red to 31 black data Window Size points Value As the cubic polynomial is stretched to fit the data in the larger window size the polynomial curve becomes smoother Note that the structure of the smaller trailing peak is no longer detected as a distinct peak from the adjacent larger peak to the right 2000 1500 Window size value of 31 black Wer Window size value of 15 red Figure 3 6 Electropherogram showing the same peaks as in the Figure 3 5 after increasing the window size value but keeping the polynomial degree the same 40 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 3 Algorithms msa Optimizing Peak Detection Sensitivity Optimizing Peak Detection Sensitivity Examples B Example 1 Reducing Window Size 41 E Example 2 Reducing Window Size Increasing Polynomial Degree ec ik cee cee Le ees ety n 42 B Example 3 Extreme Settings a 43 Example 1 Reducing Window Size Initial Figure 3 7 shows two resolved alleles of known fragment lengths Electropherogram that differ by one nucleotide detected as a single peak The analysis w
60. rrectly as well Note This method does not determine the amount of sizing accuracy error GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 49 r Chapter 3 Algorithms Size Calling Methods Classic and Advanced Modes Local Southern Method Overview The Local Southern method determines the sizes of fragments by using the reciprocal relationship between fragment length and mobility as described by E M Southern 1979 270 Best Fit 2nd Order Curve D 1 990206E 02 Al 2 075795E 01 2404 A2 1 545595E 05 R 2 1 000 180 150 120 30 60 Size Calling Curve 30 Local Southern Method T T T T T T T T T 1200 1400 1600 1800 2000 2200 2400 2600 2800 Figure 3 18 Local Southern Method Local Southern The equation attempts to describe the reciprocal relationship between Method Equation the mobility m and the length LO of the standard fragments L c m m0 LO How The Local This method which is similar to the Cubic Spline method uses the Southern Method four fragments closest in size to the unknown fragment to determine Works a best fit line value Only the region of the size ladder near the fragment of unknown length is analyzed Note Size estimates may be inaccurate if any of the standard fragments run anomalously 50 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 3 Algorithms Wu Size Calling Methods Classic and Advan
61. s Data Flow Figure 3 3 and Figure 3 4 on page 37 show the data flow in GeneMapper Software Standard signal processing is applied to the data before the data are delivered to the GeneMapper Software algorithms Input Sample Supported Applied Biosystems Genetic Analysis Systems Y Baselining Peak Detection Y Binning Y Size Matching Calling 4 Caller 1 Caller n Arbitrator Bin Assignment Final Quality Value Determination Report Results to User Figure 3 3 Microsatellite analysis data flow 36 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 3 Algorithms Wu Genotyping Algorithms Input Sample Supported Applied Biosystems Genetic Analysis Systems Y Baselining Y Peak Detection Y Binning Y Size Matching Calling Y Allele Calling Y Final Quality Value Determination v Report Results to User Figure 3 4 SNPlex System analysis data flow GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 37 msa Chapter 3 Algorithms Peak Detection Peak Detection Polynomial Degree and Peak Window Size Parameters 38 Two peak detection parameters are used in the polynomial detection algorithm Polynomial
62. s PN 4403618 and SNPlex system analysis PN 4403617 Five guides that explain how to analyze the application specific example data provided with the GeneMapper Software The guides provide brief step by step procedures for the analysis of microsatellite LOH AFLP system SNaPshot kit and SNPlex system data generated by compatible Applied Biosystems electrophoresis instruments and Data Collection Software The guides are designed to help you quickly learn to use basic functions of the GeneMapper Software GeneMapper Software Version 4 1 Online Help Describes the GeneMapper Software and provides procedures for common tasks Access online help by pressing F1 selecting Help gt Contents and Index or clicking in the toolbar of the GeneMapper window GeneMapper Software Version 4 1 Quick Reference Guide PN 4403615 Provides workflows for specific analysis types and lists instruments software and analysis applications compatible with the GeneMapper Software GeneMapper Software Version 4 1 Reference and Troubleshooting Guide PN 4403673 Provides reference information such as theory of operation and includes troubleshooting information Portable document format PDF versions of this guide and the other documents listed above are available on the GeneMapper Sofiware Version 4 1 Documentation CD Note For additional documentation see How to Obtain Support on page viii GeneMapper So
63. s for specific markers or genes within an organism verb GeneMapper Software Version 4 1 Reference and Troubleshooting Guide haploid heterozygous homozygous installation standard kit LMS locus marker matrix standard microsatellite Glossary Having one set of chromosomes and therefore having one allele per marker or gene Human egg and sperm cells are haploid Having two different alleles for a specific marker or gene Having two identical alleles for a specific marker or gene A collection of known genetic markers labeled with dyes from a specific dye set used to test the function of a genetic analyzer Within the GeneMapper Software a group of panels Linkage Mapping Set Applied Biosystems chemistry using dinucleotide repeat microsatellite markers The chromosomal location of a genetic marker or gene A known segment of DNA that has two or more allelic forms A marker exists at a known chromosomal loci and can be a gene or a non gene See also microsatellite and SNP Within the GeneMapper Software A microsatellite marker is defined by a name fragment size range bp dye color and repeat length A SNaPshot kit analysis marker is defined by a name and fragment size range bp A collection of known DNA fragments labeled with four to five different colored dyes from a specific dye set The matrix standard is run on a genetic analyzer and used for spectral calibration of sample fragments
64. s that exceed the threshold for each associated dye channel but it cannot identify peaks that fall below it Samples that exhibit low signal intensity low peak heights can occasionally fail sizing because one or more peaks fall below the threshold defined in the analysis method By lowering the threshold of the appropriate dye channel the software can call the peak s correctly In contrast when the signals of the size standard peaks are very high the software may misidentify a shoulder preceding a peak as the main peak see Figure 1 2 Because the shoulder peak does not occur at the correct position relative to the other peaks sizing fails By adjusting the analysis method so that the threshold value is greater than the height of the shoulder you can achieve good sizing Q Size Match Editor x F e Edt View Tools a xE AL M2 Size Matches size Cating Curve Sizing Quality 00 25 bp shoulder incorrectly labeled as a peak Figure 1 2 Size standard with shoulder incorrectly labeled as a peak To lower the peak detection thresholds of an analysis method 1 In the GeneMapper window click Tools GeneMapper Manager 2 In the GeneMapper Manager select the Analysis Methods tab GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 29 Chapter 1 Process Quality Values and Basic Troubleshooting SQ Sizing Quality 3 Do one of the following to Modify the current an
65. sis gt Display Plots then review the peaks at the appropriate location Symptom Possible Cause Solution PHR POV displays The sample has undergone Loss of Normal occurrence No action Check Heterozygosity LOH A difference necessary in peak heights between alleles is Further evaluate the sample for LOH using the Report Settings Editor and Report Manager expected For more information see the GeneMapper Software Version 4 1 LOH Analysis Getting Started Guide PN 4403621 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 21 wm Chapter 1 Process Quality Values and Basic Troubleshooting SFNF Sample File Not Found SFNF Sample File Not Found Description Function Expected Values Troubleshooting The SFNF PQV indicates whether or not the software can access the sample file fsa shown in the Sample File column of the associated sample When the software adds a sample to a project from a sample file it retains a link to the original file The software displays A Check in the SFNF column if the sample file is deleted renamed or moved jg Pass or A Check Determine the name and location of the missing sample file 1 In the GeneMapper window select the Samples tab 2 In the Navigation Pane of the GeneMapper window click amp to expand the contents of the project folder then select the sample that display A Check in the SFNF column 3 Select t
66. sitivity Lower Higher GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Guidelines for Use Examining Peak Definitions Effects of Varying the Polynomial Degree Chapter 3 Algorithms Peak Detection To detect well isolated baseline resolved peaks use polynomial degree values of 2 or 3 For finer control use a degree value of 4 or greater As a guideline set the peak window size in data points to be about 1 to 2 times the full width at half maximum height of the peaks that you want to detect To examine how GeneMapper Software has defined a peak select View gt Show Peak Positions The peak positions including the beginning apex and end of each peak are tick marked in the electropherogram Figure 3 5 shows peaks detected with a window size of 15 data points and a polynomial curve of degree 2 green 3 red and 4 black The diamonds represent a detected peak using the respective polynomial curves Note that the smaller trailing peak is not detected using a degree of 2 green As the peak detection window is applied to each data point across the displayed region a polynomial curve of degree 2 could not be fitted to the underlying data to detect its structure 2000 Polynomial curve of degree 4 1500 black Polynomial curve of degree 3 red 1000 Polynomial curve of degree 2 green 500 0 1 N 1 f 1 al 40 50 60 70 80 Figure 3 5 Electropherogram sh
67. southern method 52 least square method 47 local southern method 50 overview 47 size match editor definition 65 Size Standard Not Found PQV 23 size matching size calling algorithm 46 Sizing Quality PQV 25 slope threshold asymmetrical peak 45 peak end parameters 44 peak start parameters 44 SNaPshot analysis definition 66 SNF Size Standard Not Found PQV 23 SNP definition 66 SNPlex system analysis definition 66 SP Split Peak PQV 24 Index SPA Single Peak Artifact POV 24 Spectral Pull Up PQV 25 Split Peak PQV 24 SPU Spectral Pull Up PQV 25 SQ Sizing Quality PQV 25 T Technical Support contacting viii text conventions v training information on viii U user attention words described vi V varying polynomial degree 39 W window size value increasing 40 X XTLK Cross Talk PQV 32 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 69 Index 70 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Part Number 4403673 Rev A 04 2009 Applied Biosystems Applied Biosystems 850 Lincoln Centre Drive Foster City CA 94404 USA Phone 650 638 5800 Toll Free 800 345 5224 www appliedbiosystems com Technical Resources and Support For the latest technical resources and support information for all locations please refer to our Web site at www appliedbiosystems com support
68. tal distance between two peak apexes Bg Pass or A Check Select the affected genotype click iiij Analysis Display Plots then review the peak at the appropriate location SPA Single Peak Artifact Description Function Expected Values Troubleshooting The SPA PQV indicates that no peaks are present within a two base pair range before the peak for the associated genotype This PQV is designed to detect the absence of stutter peaks that accompany microsatellite peaks jg Pass or A Check Select the affected genotype click iii Analysis gt Display Plots then review the peak at the appropriate location Symptom Possible Cause Solution SPA PQV displays The GeneMapper Software did not In the Panel Manager edit the Check detect any stutter peaks to the left of marker minimum fragment length the allele peak because the then reanalyze minimum fragment length for the marker was set too high 24 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 1 Process Quality Values and Basic Troubleshooting WM SPU Spectral Pull Up SPU Spectral Pull Up Description Function Expected Values Troubleshooting The SPU PQV indicates that the apex of the peak for the associated genotype is at a position where the marker signal contains pull up peaks also called bleed through peaks Pull up peaks occur when the peak height of the called allele is less
69. that are run on the same instrument and labeled with the same dye set Microsatellite markers also known as short tandem repeats STRs are polymorphic DNA loci consisting of a repeated nucleotide sequence The repeat sequence can be from 2 to 7 base pairs long The number of repeat units varies within a population thereby creating multiple alleles for a microsatellite locus GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 63 Glossary negative control panel phenotype polymorphism positive control primer primer focus Primer Focus kit 64 A blank sample that contains no DNA but all other reagents used in the experiment It can indicate if any contamination came from sample preparation In the Sample tab of GeneMapper Software you select a Sample Type of Negative Control for the sample file generated from the negative control Additionally in the Panel Manager you define the negative control when creating markers A group of markers Within the GeneMapper Software you associate a panel with a bin set to provide bin definitions for the markers The physical manifestation of a genotype Differences between organisms or individuals DNA Variations of allele calls A sample that contains DNA with known alleles for specific markers Its purpose is to verify that the PCR amplification electrophoresis and GeneMapper Software analysis worked correctly In the Sample tab of GeneMapper S
70. the sample that displays A Check in the OS column Select the Raw Data tab to display the electropherogram of normalized spectral data collected during the associated sample run The spectral data is displayed in Relative Fluorescent Units RFU Review the data for offscale peaks o 1000 2000 3000 4000 5000 8000 8000 Offscale peak 020 primer peak 4000 2000 0 Use Table 1 3 on page 20 to determine an appropriate corrective action GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 19 dan Chapter 1 Process Quality Values and Basic Troubleshooting OVL Overlap Table 1 3 OS PQV Troubleshooting Symptom Possible Cause Solution e MNF POV in the Samples tab displays Too much size No action necessary The data Check standard injected cannot be manipulated to remove e Raw data contains multiple off scale into the capillary the oversized peaks size standard peaks Decrease the quantity of size standard used in subsequent runs Also make sure to use Hi Di Formamide as the loading reagent IMPORTANT Water loading can produce artificially high signal and is not recommended e MNF PQV in the Genotypes tab Too much sample No action necessary The data displays Check injected into the cannot be manipulated to remove Raw data contains multiple off scale Capillary the oversized peaks peaks in the signal s associated with Decrease the quantity of sample the sample
71. trophoresis gene genome genotype 62 See positive control on page 64 See negative control on page 64 The security group assigned to an Access Control administrative identifier user user group security group profile This security group is used to determine access by a user to the administrative data in the Administrative GUI and API The part of access control that administers access to user data See Security Group Properties that define the type of access a user has to a piece of data One form of offline storage Having two sets of chromosomes and therefore having two alleles per marker or gene Human cells other than egg and sperm cells are diploid A set of four to five different colored dyes A specific dye set is used to label DNA fragments or markers in matrix standards installation standards and chemistry kits A graphical representation of the intensity y axis of bands produced in a single gel lane or capillary as a function of time x axis A technique used to separate molecules by using an electric field to pass those molecules through a porous matrix The basic unit of heredity that carries the genetic information for a given RNA molecule or protein All the DNA contained in an organism or cell including both the chromosomes in the nucleus and the DNA in the mitochondria The set of allele calls for specific markers or genes within an organism noun To determine the allele call
72. tween the mobility m and the length LO of the standard fragments L c m m0 LO The fitting constants LO m0 and c iti c mi m0 L0 are calculated by a least squares fit to minimize the left side quantity All points in the standard are weighted equally and the curve is not constrained to go through any specific point The software can analyze a large range of fragment sizes with this method For best results use a standard that brackets all the fragments of interest GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Ch 3 Algorith Allele Calling Algorithms Overview Final allele calls are based on a consensus between a variety of different allele calling algorithms Each caller has a different design philosophy such that it excels in a particular data regime but not in others Allele calling algorithms involve envelope detection optimization of parametric models and rule based systems Types of Allele The GeneMapper Software provides following allele calling methods Calling Methods B Microsatellite Analysis Methods 0005 54 B SNPlex System Analysis Methods lusu 55 Rules Genotyping Algorithm see page 55 Model Genotyping Algorithm see page 56 GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 53 Chapter 3 Algorithms Allele Calling Algorithms Microsatellite Analysis Methods Example Output of
73. typing System 48 Plex User Guide PN 4360856 describes how to resolve common chemistry and software related problems The user guide addresses all aspects of the SNPlex System not just those issues that are exclusive to the GeneMapper Software analysis GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 3 Algorithms In this chapter deri o haa B Genotyping Algorithms 02 002 0 ee 36 peepee B Peak Detection 0 cee eee eee eee 38 B Optimizing Peak Detection Sensitivity 4 B Slope Thresholds for Peak Start End Parameters 44 B Slope Threshold Example 002 0200 eae 45 B Size Matching Size Calling Algorithm 46 E Size Calling Methods Classic and Advanced Modes 47 SNPlex System B Allele Calling Algorithms 0 0000 eee 53 Troubleshooting Algorithms GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 35 tales Chapter 3 Algorithms Genotyping Algorithms Genotyping Algorithms Overview This chapter discusses the following algorithms Peak Detection Uses the Basic Advanced or Classic mode algorithms to detect peaks and process data Size Matching Calling Matches detected peaks to size standards Binning Determines bin centers for genotyping Allele Calling Produces a consensus call based on several allele calling algorithm
74. ured user group that cannot be removed and that is always associated with the Admin security group A pre configured user that cannot be removed and that is always associated with the Admin user group Amplified Fragment Length Polymorphism A DNA fingerprinting technique that allows the comparison of the DNA from different organisms DNA fragments of varying lengths are created by cleaving an organism s DNA with restriction enzymes a specific subset of these fragments are amplified and analyzed for comparison purposes GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 59 Glossary Algorithm All User group allele allele call allele calling allelic ladder analysis method analysis parameters 60 A set of ordered steps for solving a problem such as a mathematical formula or the instructions in a program The terms algorithm and logic are synonymous where both refer to a sequence of steps to solve a problem However an algorithm is an expression that solves a complex problem rather than the overall input process output logic of typical business programs A user group that contains all users A user cannot be disassociated from the user group One of two or more alternate forms of a marker or gene Reference alleles are all alleles or bins created in a bin set in the GeneMapper Software They are denoted by a red cross hatch in the Panel Manager Project alleles are all alleles detected
75. ut of Bin Allele The BIN PQV indicates that the apex of the peak for the associated genotype is outside of the boundaries that define the associated bin Note When the BIN PQV is triggered the software reduces the GQ PQV by 80 because the default multiplier is 0 8 Note For human identification HID analysis the BIN PQV is displayed as the OL Off Ladder Alleles PQV Bg Pass or Check Select the affected genotype click iiij Analysis gt Display Plots then review the allele s at the appropriate bin location Symptom Possible Cause Solution BIN PQV displays After using Auto Bin to generate bins Correct the SPA flag by editing the Check the software did not create a bin foran marker minimum fragment length allele peak because it considered the then reanalyze and perform the Auto peak to be a single peak artifact The Bin again SPA flag was triggered because the software did not detect any stutter peaks to the left of the allele peak a result of the minimum fragment length for the marker being set too high The GeneMapper Software detected In the Samples tab of the an allele peak that did not fit into any GeneMapper window set the Sample of the defined bins because the bins Type of the sample containing the were not calibrated to the allelic allelic ladder to Allelic Ladder ladder a result of a sample file containing an allelic ladder that is not designated as an all
76. y repeated measurements of a given sample or allele systematic variations from this a priori model can be quantified and later removed In the process of normalization an overall scale factor can be selected which can be set to one This utility is demonstrated in Figure 3 22 on page 57 where the output from 20 runs can be overlaid to understand the random as opposed to systematic behavior of signals For each run 2 ee 48 co 96 9216 measurements were normalized with 193 parameters or approximately one parameter for each 48 measurements GeneMapper Software Version 4 1 Reference and Troubleshooting Guide Chapter 3 Algorithms Allele Calling Algorithms TP t For ET 0 matt 0 0 05 1 15 0 05 Figure 3 22 Pre and post normalization data Shown are 80 000 measurements of SNPlex System data overlaid in normalized allele coordinates In the left plot data are normalized only by run In the right the data are fully normalized by sample and allele Genotyping The random error remaining in the data after normalization is relatively small when compared with the separation of the genotype coordinates as seen in Figure 3 23 Each measurement is assigned the genotype for which it has the highest probability Allele 1 Homozygotes Heterozygotes Allele 2 Homozygotes Figure 3 23 Training data distribution GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 57 58 Chapter 3 Algorithms Alle
77. ype call has been edited GeneMapper Software Version 4 1 Reference and Troubleshooting Guide AN Allele Number Description Function Expected Values Troubleshooting Chapter 1 Process Quality Values and Basic Troubleshooting S AN Allele Number The AN PQV indicates that the associated sample contains either Anumber of alleles at the specified marker that exceeds the Max Expected Alleles setting in the Peak Quality tab of the analysis method or No alleles are present at the specified marker J Pass or A Check Select the affected genotype s click W Analysis gt Display Plots then review the sample data at the affected marker for additional peaks or for the absence of peaks BD Broad Peak Description Function Expected Values Troubleshooting The BD PQV indicates that the width of the peak for the associated genotype exceeds the Max peak width setting in the Peak Quality tab of the analysis method Note When the BD PQV is triggered the software reduces the GQ PQV by 50 because the default multiplier is 0 5 J Pass or A Check Select the affected genotype click W Analysis Display Plots then review the associated peak for irregularities GeneMapper Software Version 4 1 Reference and Troubleshooting Guide 9 BIN Out of Bin Allele Description Function Expected Values Troubleshooting Chapter 1 Process Quality Values and Basic Troubleshooting BIN O

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